Examples of weka.classifiers.bayes.net.ParentSet

weka.classifiers.bayes.net.ParentSet
Helper class for Bayes Network classifiers. Provides datastructures to represent a set of parents in a graph. @author Remco Bouckaert (rrb@xm.co.nz) @version $Revision: 6943 $

            // find a node for which all parents are 'done'
            boolean bFound = false;


            for (int iNode2 = 0; !bFound && iNode2 < nNodes; iNode2++) {
                if (!bDone[iNode2]) {
                  ParentSet parentSet = bayesNet.getParentSet(iNode2);
                    boolean bHasNoParents = true;
                    for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
                        if (!bDone[parentSet.getParent(iParent)]) {
                          
                            // this one has a parent which is not 'done' UNLESS it is the arc to be reversed
                            if (!(iNode2 == iAttributeHead && parentSet.getParent(iParent) == iAttributeTail)) {
                                bHasNoParents = false;
                            }
                        }
                    }

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     * @param instances
     */
    protected void doMarkovBlanketCorrection(BayesNet bayesNet, Instances instances) {
        // Add class node as parent if it is not in the Markov Boundary
        int iClass = instances.classIndex();
        ParentSet ancestors = new ParentSet();
        int nOldSize = 0;
        ancestors.addParent(iClass, instances);
        while (nOldSize != ancestors.getNrOfParents()) {
            nOldSize = ancestors.getNrOfParents();
            for (int iNode = 0; iNode < nOldSize; iNode++) {
                int iCurrent = ancestors.getParent(iNode);
                ParentSet p = bayesNet.getParentSet(iCurrent);
                for (int iParent = 0; iParent < p.getNrOfParents(); iParent++) {
                    if (!ancestors.contains(p.getParent(iParent))) {
                        ancestors.addParent(p.getParent(iParent), instances);
                    }
                }
            }
        }
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {

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   * @param iHead
   * @param iTail
   * @param instances
   */
  void applyArcAddition(BayesNet bayesNet, int iHead, int iTail, Instances instances) {
    ParentSet bestParentSet = bayesNet.getParentSet(iHead);
    bestParentSet.addParent(iTail, instances);
    updateCache(iHead, instances.numAttributes(), bestParentSet);
  } // applyArcAddition

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   * @param iHead
   * @param iTail
   * @param instances
   */
  void applyArcDeletion(BayesNet bayesNet, int iHead, int iTail, Instances instances) {
    ParentSet bestParentSet = bayesNet.getParentSet(iHead);
    bestParentSet.deleteParent(iTail, instances);
    updateCache(iHead, instances.numAttributes(), bestParentSet);
  } // applyArcAddition

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   */
  Operation findBestArcToDelete(BayesNet bayesNet, Instances instances, Operation oBestOperation) {
    int nNrOfAtts = instances.numAttributes();
    // find best arc to delete
    for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
      ParentSet parentSet = bayesNet.getParentSet(iNode);
      for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
        Operation oOperation = new Operation(parentSet.getParent(iParent), iNode, Operation.OPERATION_DEL);
        if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
          if (isNotTabu(oOperation)) {
            oBestOperation = oOperation;
            oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
          }

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   */
  Operation findBestArcToReverse(BayesNet bayesNet, Instances instances, Operation oBestOperation) {
    int nNrOfAtts = instances.numAttributes();
    // find best arc to reverse
    for (int iNode = 0; iNode < nNrOfAtts; iNode++) {
      ParentSet parentSet = bayesNet.getParentSet(iNode);
      for (int iParent = 0; iParent < parentSet.getNrOfParents(); iParent++) {
        int iTail = parentSet.getParent(iParent);
        // is reversal allowed?
        if (reverseArcMakesSense(bayesNet, instances, iNode, iTail) && 
            bayesNet.getParentSet(iTail).getNrOfParents() < m_nMaxNrOfParents) {
          // go check if reversal results in the best step forward
          Operation oOperation = new Operation(parentSet.getParent(iParent), iNode, Operation.OPERATION_REVERSE);
          if (m_Cache.get(oOperation) > oBestOperation.m_fDeltaScore) {
            if (isNotTabu(oOperation)) {
              oBestOperation = oOperation;
              oBestOperation.m_fDeltaScore = m_Cache.get(oOperation);
            }

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   * @param nNode node for which the score is calculate
   * @return log score
   */
  private double calcNodeScoreADTree(int nNode) {
    Instances instances = m_BayesNet.m_Instances;
    ParentSet oParentSet = m_BayesNet.getParentSet(nNode);
    // get set of parents, insert iNode
    int nNrOfParents = oParentSet.getNrOfParents();
    int[] nNodes = new int[nNrOfParents + 1];
    for (int iParent = 0; iParent < nNrOfParents; iParent++) {
      nNodes[iParent] = oParentSet.getParent(iParent);
    }
    nNodes[nNrOfParents] = nNode;


    // calculate offsets
    int[] nOffsets = new int[nNrOfParents + 1];
    int nOffset = 1;
    nOffsets[nNrOfParents] = 1;
    nOffset *= instances.attribute(nNode).numValues();
    for (int iNode = nNrOfParents - 1; iNode >= 0; iNode--) {
      nOffsets[iNode] = nOffset;
      nOffset *= instances.attribute(nNodes[iNode]).numValues();
    }


    // sort nNodes & offsets
    for (int iNode = 1; iNode < nNodes.length; iNode++) {
      int iNode2 = iNode;
      while (iNode2 > 0 && nNodes[iNode2] < nNodes[iNode2 - 1]) {
        int h = nNodes[iNode2];
        nNodes[iNode2] = nNodes[iNode2 - 1];
        nNodes[iNode2 - 1] = h;
        h = nOffsets[iNode2];
        nOffsets[iNode2] = nOffsets[iNode2 - 1];
        nOffsets[iNode2 - 1] = h;
        iNode2--;
      }
    }


    // get counts from ADTree
    int nCardinality = oParentSet.getCardinalityOfParents();
    int numValues = instances.attribute(nNode).numValues();
    int[] nCounts = new int[nCardinality * numValues];
    //if (nNrOfParents > 1) {


    m_BayesNet.getADTree().getCounts(nCounts, nNodes, nOffsets, 0, 0, false);

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    return calcScoreOfCounts(nCounts, nCardinality, numValues, instances);
  } // CalcNodeScore


  private double calcNodeScorePlain(int nNode) {
    Instances instances = m_BayesNet.m_Instances;
    ParentSet oParentSet = m_BayesNet.getParentSet(nNode);


    // determine cardinality of parent set & reserve space for frequency counts
    int nCardinality = oParentSet.getCardinalityOfParents();
    int numValues = instances.attribute(nNode).numValues();
    int[] nCounts = new int[nCardinality * numValues];


    // initialize (don't need this?)
    for (int iParent = 0; iParent < nCardinality * numValues; iParent++) {
      nCounts[iParent] = 0;
    }


    // estimate distributions
    Enumeration enumInsts = instances.enumerateInstances();


    while (enumInsts.hasMoreElements()) {
      Instance instance = (Instance) enumInsts.nextElement();


      // updateClassifier;
      double iCPT = 0;


      for (int iParent = 0; iParent < oParentSet.getNrOfParents(); iParent++) {
        int nParent = oParentSet.getParent(iParent);


        iCPT = iCPT * instances.attribute(nParent).numValues() + instance.value(nParent);
      }


      nCounts[numValues * ((int) iCPT) + (int) instance.value(nNode)]++;

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   * @param nNode node for which the score is calculate
   * @param nCandidateParent candidate parent to add to the existing parent set
   * @return log score
   */
  public double calcScoreWithExtraParent(int nNode, int nCandidateParent) {
    ParentSet oParentSet = m_BayesNet.getParentSet(nNode);


    // sanity check: nCandidateParent should not be in parent set already
    if (oParentSet.contains(nCandidateParent)) {
        return -1e100;
    }


    // set up candidate parent
    oParentSet.addParent(nCandidateParent, m_BayesNet.m_Instances);


    // calculate the score
    double logScore = calcNodeScore(nNode);


    // delete temporarily added parent
    oParentSet.deleteLastParent(m_BayesNet.m_Instances);


    return logScore;
  } // CalcScoreWithExtraParent

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   * @param nNode node for which the score is calculate
   * @param nCandidateParent candidate parent to delete from the existing parent set
   * @return log score
   */
  public double calcScoreWithMissingParent(int nNode, int nCandidateParent) {
    ParentSet oParentSet = m_BayesNet.getParentSet(nNode);


    // sanity check: nCandidateParent should be in parent set already
    if (!oParentSet.contains( nCandidateParent)) {
        return -1e100;
    }


    // set up candidate parent
    int iParent = oParentSet.deleteParent(nCandidateParent, m_BayesNet.m_Instances);


    // calculate the score
    double logScore = calcNodeScore(nNode);


    // restore temporarily deleted parent
    oParentSet.addParent(nCandidateParent, iParent, m_BayesNet.m_Instances);


    return logScore;
  } // CalcScoreWithMissingParent

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